Evidence map›Paper›PMID 41977066›Full record

ReviewJournal of clinical medicine2026

Artificial Intelligence in Head and Neck Surgical Oncology: A State-of-the-Art Review.

Steven X Chen, Maria Feucht, Aditya Bhatt, Janice L Farlow

Abstract readReview
In one paragraph

Review in Journal of clinical medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Oral rehabilitation of patients with cancer: challenges following surgical resection.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Steven X ChenDepartment of Otolaryngology-Head and Neck Surgery, Indiana University School of Medicine, Indiana University, Indianapolis, IN 46202, USA.
Maria FeuchtDepartment of Otolaryngology-Head and Neck Surgery, Indiana University School of Medicine, Indiana University, Indianapolis, IN 46202, USA.
Aditya BhattDepartment of Otolaryngology-Head and Neck Surgery, Indiana University School of Medicine, Indiana University, Indianapolis, IN 46202, USA.ORCID 0009-0003-2111-3813
Janice L FarlowDepartment of Otolaryngology-Head and Neck Surgery, Indiana University School of Medicine, Indiana University, Indianapolis, IN 46202, USA.ORCID 0000-0003-1214-4960

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is rapidly reshaping head and neck surgical oncology by augmenting decision-making across the full perioperative continuum. This state-of-the-art review aims to provide head and neck surgical oncologists with a conceptual framework for understanding and critically appraising AI tools entering clinical practice, summarizing how machine learning, deep learning, and generative AI are being integrated into contemporary surgical workflows. Preoperative applications include detection of occult nodal metastasis and extranodal extension. Intraoperative innovations include augmented reality-assisted navigation, real-time margin assessment, and improving visual clarity and tissue handling for robotic platforms. Postoperatively, AI can predict complications like free flap failure and oncologic outcomes. Large language models are being operationalized for clinician-facing applications such as documentation and inbox support, as well as patient-facing education. Despite promising results, broad clinical deployment remains limited by concerns about privacy, validation, reliability, safety, and ethics. Widespread adoption will require prospective clinical trials, robust governance, and human-centered workflows that ensure AI remains a safe, assistive copilot.

Indexed as

artificial intelligenceaugmented realitycomputer visiondeep learninghead and necklarge language modelmachine learningoncologyotorhinolaryngologysurgery

Identifiers

PMID41977066
PMCPMC13073410

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.